Overview

Dataset statistics

Number of variables70
Number of observations260601
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.1 MiB
Average record size in memory294.0 B

Variable types

BOOL60
NUM9
CAT1

Warnings

building_id has unique values Unique
geo_level_1_id has 4011 (1.5%) zeros Zeros
age has 26041 (10.0%) zeros Zeros
count_families has 20862 (8.0%) zeros Zeros

Reproduction

Analysis started2020-09-29 03:17:06.759523
Analysis finished2020-09-29 03:19:35.431822
Duration2 minutes and 28.67 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

building_id
Real number (ℝ≥0)

UNIQUE

Distinct260601
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525675.4828
Minimum4
Maximum1052934
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2020-09-29T11:19:35.635265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile52114
Q1261190
median525757
Q3789762
95-th percentile1000724
Maximum1052934
Range1052930
Interquartile range (IQR)528572

Descriptive statistics

Standard deviation304544.999
Coefficient of variation (CV)0.5793403136
Kurtosis-1.203878964
Mean525675.4828
Median Absolute Deviation (MAD)264277
Skewness0.001882356737
Sum1.369915565e+11
Variance9.274765644e+10
MonotocityNot monotonic
2020-09-29T11:19:35.790287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10526701< 0.1%
 
8473041< 0.1%
 
3681021< 0.1%
 
7299861< 0.1%
 
9005781< 0.1%
 
8964801< 0.1%
 
8084151< 0.1%
 
8125051< 0.1%
 
2902641< 0.1%
 
2697821< 0.1%
 
Other values (260591)260591> 99.9%
 
ValueCountFrequency (%) 
41< 0.1%
 
81< 0.1%
 
121< 0.1%
 
161< 0.1%
 
171< 0.1%
 
ValueCountFrequency (%) 
10529341< 0.1%
 
10529311< 0.1%
 
10529291< 0.1%
 
10529261< 0.1%
 
10529211< 0.1%
 

geo_level_1_id
Real number (ℝ≥0)

ZEROS

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.90035341
Minimum0
Maximum30
Zeros4011
Zeros (%)1.5%
Memory size2.0 MiB
2020-09-29T11:19:35.929070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median12
Q321
95-th percentile27
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.033616625
Coefficient of variation (CV)0.5779433361
Kurtosis-1.213248785
Mean13.90035341
Median Absolute Deviation (MAD)6
Skewness0.2725303548
Sum3622446
Variance64.53899608
MonotocityNot monotonic
2020-09-29T11:19:36.050772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
6243819.4%
 
26226158.7%
 
10220798.5%
 
17218138.4%
 
8190807.3%
 
7189947.3%
 
20172166.6%
 
21148895.7%
 
4145685.6%
 
27125324.8%
 
Other values (21)7243427.8%
 
ValueCountFrequency (%) 
040111.5%
 
127011.0%
 
29310.4%
 
375402.9%
 
4145685.6%
 
ValueCountFrequency (%) 
3026861.0%
 
293960.2%
 
282650.1%
 
27125324.8%
 
26226158.7%
 

geo_level_2_id
Real number (ℝ≥0)

Distinct1414
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.0746851
Minimum0
Maximum1427
Zeros38
Zeros (%)< 0.1%
Memory size2.0 MiB
2020-09-29T11:19:36.191689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69
Q1350
median702
Q31050
95-th percentile1377
Maximum1427
Range1427
Interquartile range (IQR)700

Descriptive statistics

Standard deviation412.7107336
Coefficient of variation (CV)0.5886829782
Kurtosis-1.188232475
Mean701.0746851
Median Absolute Deviation (MAD)349
Skewness0.02895738139
Sum182700764
Variance170330.1496
MonotocityNot monotonic
2020-09-29T11:19:36.499859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3940381.5%
 
15825201.0%
 
18120800.8%
 
138720400.8%
 
15718970.7%
 
36317600.7%
 
46317400.7%
 
67317040.7%
 
53316840.6%
 
88316260.6%
 
Other values (1404)23951291.9%
 
ValueCountFrequency (%) 
038< 0.1%
 
12040.1%
 
377< 0.1%
 
43150.1%
 
525< 0.1%
 
ValueCountFrequency (%) 
14276< 0.1%
 
14262860.1%
 
14254660.2%
 
14247< 0.1%
 
14233< 0.1%
 

geo_level_3_id
Real number (ℝ≥0)

Distinct11595
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6257.876148
Minimum0
Maximum12567
Zeros2
Zeros (%)< 0.1%
Memory size2.0 MiB
2020-09-29T11:19:36.671269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile611
Q13073
median6270
Q39412
95-th percentile11927
Maximum12567
Range12567
Interquartile range (IQR)6339

Descriptive statistics

Standard deviation3646.369645
Coefficient of variation (CV)0.5826848532
Kurtosis-1.213896506
Mean6257.876148
Median Absolute Deviation (MAD)3171
Skewness0.0003935120899
Sum1630808782
Variance13296011.59
MonotocityNot monotonic
2020-09-29T11:19:36.831459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6336510.2%
 
91336470.2%
 
6215300.2%
 
112464700.2%
 
20054660.2%
 
114404550.2%
 
77234430.2%
 
92293810.1%
 
24523490.1%
 
122583120.1%
 
Other values (11585)25589798.2%
 
ValueCountFrequency (%) 
02< 0.1%
 
16< 0.1%
 
39< 0.1%
 
514< 0.1%
 
621< 0.1%
 
ValueCountFrequency (%) 
125671< 0.1%
 
125657< 0.1%
 
125646< 0.1%
 
1256324< 0.1%
 
125623< 0.1%
 

count_floors_pre_eq
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.129723217
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2020-09-29T11:19:36.957192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7276645453
Coefficient of variation (CV)0.3416709456
Kurtosis2.322597881
Mean2.129723217
Median Absolute Deviation (MAD)0
Skewness0.8341129586
Sum555008
Variance0.5294956905
MonotocityNot monotonic
2020-09-29T11:19:37.064674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
215662360.1%
 
35561721.3%
 
14044115.5%
 
454242.1%
 
522460.9%
 
62090.1%
 
739< 0.1%
 
91< 0.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
14044115.5%
 
215662360.1%
 
35561721.3%
 
454242.1%
 
522460.9%
 
ValueCountFrequency (%) 
91< 0.1%
 
81< 0.1%
 
739< 0.1%
 
62090.1%
 
522460.9%
 

age
Real number (ℝ≥0)

ZEROS

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.53502865
Minimum0
Maximum995
Zeros26041
Zeros (%)10.0%
Memory size2.0 MiB
2020-09-29T11:19:37.202537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median15
Q330
95-th percentile60
Maximum995
Range995
Interquartile range (IQR)20

Descriptive statistics

Standard deviation73.56593652
Coefficient of variation (CV)2.772408408
Kurtosis157.2482363
Mean26.53502865
Median Absolute Deviation (MAD)10
Skewness12.19249422
Sum6915055
Variance5411.947016
MonotocityNot monotonic
2020-09-29T11:19:37.350460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
103889614.9%
 
153601013.8%
 
53369712.9%
 
203218212.3%
 
02604110.0%
 
25243669.3%
 
30180286.9%
 
35107104.1%
 
40105594.1%
 
5072572.8%
 
Other values (32)228558.8%
 
ValueCountFrequency (%) 
02604110.0%
 
53369712.9%
 
103889614.9%
 
153601013.8%
 
203218212.3%
 
ValueCountFrequency (%) 
99513900.5%
 
200106< 0.1%
 
1952< 0.1%
 
1903< 0.1%
 
1851< 0.1%
 

area_percentage
Real number (ℝ≥0)

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.018050583
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2020-09-29T11:19:37.504342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q39
95-th percentile16
Maximum100
Range99
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.392230936
Coefficient of variation (CV)0.5477928694
Kurtosis30.43825794
Mean8.018050583
Median Absolute Deviation (MAD)2
Skewness3.526082314
Sum2089512
Variance19.29169259
MonotocityNot monotonic
2020-09-29T11:19:37.658490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
64201316.1%
 
73675214.1%
 
53272412.6%
 
82844510.9%
 
9221998.5%
 
4192367.4%
 
10156136.0%
 
11139075.3%
 
3118374.5%
 
1275812.9%
 
Other values (74)3029411.6%
 
ValueCountFrequency (%) 
190< 0.1%
 
231811.2%
 
3118374.5%
 
4192367.4%
 
53272412.6%
 
ValueCountFrequency (%) 
1001< 0.1%
 
963< 0.1%
 
901< 0.1%
 
865< 0.1%
 
854< 0.1%
 

height_percentage
Real number (ℝ≥0)

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.434365179
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Memory size2.0 MiB
2020-09-29T11:19:37.792608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q36
95-th percentile9
Maximum32
Range30
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.918418221
Coefficient of variation (CV)0.3530160667
Kurtosis14.31852616
Mean5.434365179
Median Absolute Deviation (MAD)1
Skewness1.808261757
Sum1416201
Variance3.68032847
MonotocityNot monotonic
2020-09-29T11:19:37.909628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%) 
57851330.1%
 
64647717.8%
 
43776314.5%
 
73546513.6%
 
32595710.0%
 
8139025.3%
 
293053.6%
 
953762.1%
 
1044921.7%
 
119170.4%
 
Other values (17)24340.9%
 
ValueCountFrequency (%) 
293053.6%
 
32595710.0%
 
43776314.5%
 
57851330.1%
 
64647717.8%
 
ValueCountFrequency (%) 
3275< 0.1%
 
311< 0.1%
 
282< 0.1%
 
262< 0.1%
 
253< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
237500 
1
 
23101
ValueCountFrequency (%) 
023750091.1%
 
1231018.9%
 
2020-09-29T11:19:38.000186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
1
198561 
0
62040 
ValueCountFrequency (%) 
119856176.2%
 
06204023.8%
 
2020-09-29T11:19:38.043376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
251654 
1
 
8947
ValueCountFrequency (%) 
025165496.6%
 
189473.4%
 
2020-09-29T11:19:38.088925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
255849 
1
 
4752
ValueCountFrequency (%) 
025584998.2%
 
147521.8%
 
2020-09-29T11:19:38.134153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
242840 
1
 
17761
ValueCountFrequency (%) 
024284093.2%
 
1177616.8%
 
2020-09-29T11:19:38.179620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
240986 
1
 
19615
ValueCountFrequency (%) 
024098692.5%
 
1196157.5%
 
2020-09-29T11:19:38.227403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
194151 
1
66450 
ValueCountFrequency (%) 
019415174.5%
 
16645025.5%
 
2020-09-29T11:19:38.272301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
238447 
1
 
22154
ValueCountFrequency (%) 
023844791.5%
 
1221548.5%
 
2020-09-29T11:19:38.319072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
249502 
1
 
11099
ValueCountFrequency (%) 
024950295.7%
 
1110994.3%
 
2020-09-29T11:19:38.367443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
256468 
1
 
4133
ValueCountFrequency (%) 
025646898.4%
 
141331.6%
 
2020-09-29T11:19:38.414588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
256696 
1
 
3905
ValueCountFrequency (%) 
025669698.5%
 
139051.5%
 
2020-09-29T11:19:38.462407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

count_families
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9839486418
Minimum0
Maximum9
Zeros20862
Zeros (%)8.0%
Memory size2.0 MiB
2020-09-29T11:19:38.545399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4183889779
Coefficient of variation (CV)0.425214244
Kurtosis17.67094319
Mean0.9839486418
Median Absolute Deviation (MAD)0
Skewness1.634757873
Sum256418
Variance0.1750493368
MonotocityNot monotonic
2020-09-29T11:19:38.644331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
122611586.8%
 
0208628.0%
 
2112944.3%
 
318020.7%
 
43890.1%
 
5104< 0.1%
 
622< 0.1%
 
77< 0.1%
 
94< 0.1%
 
82< 0.1%
 
ValueCountFrequency (%) 
0208628.0%
 
122611586.8%
 
2112944.3%
 
318020.7%
 
43890.1%
 
ValueCountFrequency (%) 
94< 0.1%
 
82< 0.1%
 
77< 0.1%
 
622< 0.1%
 
5104< 0.1%
 
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
231445 
1
29156 
ValueCountFrequency (%) 
023144588.8%
 
12915611.2%
 
2020-09-29T11:19:38.720762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
243824 
1
 
16777
ValueCountFrequency (%) 
024382493.6%
 
1167776.4%
 
2020-09-29T11:19:38.764831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
251838 
1
 
8763
ValueCountFrequency (%) 
025183896.6%
 
187633.4%
 
2020-09-29T11:19:38.809512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
258490 
1
 
2111
ValueCountFrequency (%) 
025849099.2%
 
121110.8%
 
2020-09-29T11:19:38.852864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260356 
1
 
245
ValueCountFrequency (%) 
026035699.9%
 
12450.1%
 
2020-09-29T11:19:38.899681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260507 
1
 
94
ValueCountFrequency (%) 
0260507> 99.9%
 
194< 0.1%
 
2020-09-29T11:19:38.943833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260322 
1
 
279
ValueCountFrequency (%) 
026032299.9%
 
12790.1%
 
2020-09-29T11:19:38.988090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260552 
1
 
49
ValueCountFrequency (%) 
0260552> 99.9%
 
149< 0.1%
 
2020-09-29T11:19:39.031666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260563 
1
 
38
ValueCountFrequency (%) 
0260563> 99.9%
 
138< 0.1%
 
2020-09-29T11:19:39.076609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
260578 
1
 
23
ValueCountFrequency (%) 
0260578> 99.9%
 
123< 0.1%
 
2020-09-29T11:19:39.120629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
0
259267 
1
 
1334
ValueCountFrequency (%) 
025926799.5%
 
113340.5%
 
2020-09-29T11:19:39.164394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

damage_grade
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
2
148259 
3
87218 
1
25124 
ValueCountFrequency (%) 
214825956.9%
 
38721833.5%
 
1251249.6%
 
2020-09-29T11:19:39.251911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-29T11:19:39.330877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:39.416701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
225073 
1
35528 
ValueCountFrequency (%) 
022507386.4%
 
13552813.6%
 
2020-09-29T11:19:39.489658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
252285 
1
 
8316
ValueCountFrequency (%) 
025228596.8%
 
183163.2%
 
2020-09-29T11:19:39.536196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
216757 
0
43844 
ValueCountFrequency (%) 
121675783.2%
 
04384416.8%
 
2020-09-29T11:19:39.579503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
259153 
1
 
1448
ValueCountFrequency (%) 
025915399.4%
 
114480.6%
 
2020-09-29T11:19:39.622996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
250022 
1
 
10579
ValueCountFrequency (%) 
025002295.9%
 
1105794.1%
 
2020-09-29T11:19:39.669482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
219196 
0
41405 
ValueCountFrequency (%) 
121919684.1%
 
04140515.9%
 
2020-09-29T11:19:39.914546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
246341 
1
 
14260
ValueCountFrequency (%) 
024634194.5%
 
1142605.5%
 
2020-09-29T11:19:39.961089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
245483 
1
 
15118
ValueCountFrequency (%) 
024548394.2%
 
1151185.8%
 
2020-09-29T11:19:40.005036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
182842 
0
77759 
ValueCountFrequency (%) 
118284270.2%
 
07775929.8%
 
2020-09-29T11:19:40.050946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
199025 
1
61576 
ValueCountFrequency (%) 
019902576.4%
 
16157623.6%
 
2020-09-29T11:19:40.095483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
244418 
1
 
16183
ValueCountFrequency (%) 
024441893.8%
 
1161836.2%
 
2020-09-29T11:19:40.139006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
209619 
0
50982 
ValueCountFrequency (%) 
120961980.4%
 
05098219.6%
 
2020-09-29T11:19:40.183923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260093 
1
 
508
ValueCountFrequency (%) 
026009399.8%
 
15080.2%
 
2020-09-29T11:19:40.229315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
236008 
1
24593 
ValueCountFrequency (%) 
023600890.6%
 
1245939.4%
 
2020-09-29T11:19:40.275481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
235724 
1
24877 
ValueCountFrequency (%) 
023572490.5%
 
1248779.5%
 
2020-09-29T11:19:40.319910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
259597 
1
 
1004
ValueCountFrequency (%) 
025959799.6%
 
110040.4%
 
2020-09-29T11:19:40.363890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
220758 
1
39843 
ValueCountFrequency (%) 
022075884.7%
 
13984315.3%
 
2020-09-29T11:19:40.418125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
165282 
0
95319 
ValueCountFrequency (%) 
116528263.4%
 
09531936.6%
 
2020-09-29T11:19:40.463466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
248573 
1
 
12028
ValueCountFrequency (%) 
024857395.4%
 
1120284.6%
 
2020-09-29T11:19:40.508512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
217153 
1
43448 
ValueCountFrequency (%) 
021715383.3%
 
14344816.7%
 
2020-09-29T11:19:40.558307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

position_j
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
247319 
1
 
13282
ValueCountFrequency (%) 
024731994.9%
 
1132825.1%
 
2020-09-29T11:19:40.604528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

position_o
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
258268 
1
 
2333
ValueCountFrequency (%) 
025826899.1%
 
123330.9%
 
2020-09-29T11:19:40.650892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

position_s
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
202090 
0
58511 
ValueCountFrequency (%) 
120209077.5%
 
05851122.5%
 
2020-09-29T11:19:40.696538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

position_t
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
217705 
1
42896 
ValueCountFrequency (%) 
021770583.5%
 
14289616.5%
 
2020-09-29T11:19:40.742051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260349 
1
 
252
ValueCountFrequency (%) 
026034999.9%
 
12520.1%
 
2020-09-29T11:19:40.788837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260276 
1
 
325
ValueCountFrequency (%) 
026027699.9%
 
13250.1%
 
2020-09-29T11:19:40.836085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
250072 
0
 
10529
ValueCountFrequency (%) 
125007296.0%
 
0105294.0%
 
2020-09-29T11:19:40.881673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260579 
1
 
22
ValueCountFrequency (%) 
0260579> 99.9%
 
122< 0.1%
 
2020-09-29T11:19:40.928275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260555 
1
 
46
ValueCountFrequency (%) 
0260555> 99.9%
 
146< 0.1%
 
2020-09-29T11:19:40.973139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260563 
1
 
38
ValueCountFrequency (%) 
0260563> 99.9%
 
138< 0.1%
 
2020-09-29T11:19:41.018512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260442 
1
 
159
ValueCountFrequency (%) 
026044299.9%
 
11590.1%
 
2020-09-29T11:19:41.063078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
254909 
1
 
5692
ValueCountFrequency (%) 
025490997.8%
 
156922.2%
 
2020-09-29T11:19:41.107920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
260255 
1
 
346
ValueCountFrequency (%) 
026025599.9%
 
13460.1%
 
2020-09-29T11:19:41.153254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
256952 
1
 
3649
ValueCountFrequency (%) 
025695298.6%
 
136491.4%
 
2020-09-29T11:19:41.199088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
255089 
1
 
5512
ValueCountFrequency (%) 
025508997.9%
 
155122.1%
 
2020-09-29T11:19:41.243776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
259128 
1
 
1473
ValueCountFrequency (%) 
025912899.4%
 
114730.6%
 
2020-09-29T11:19:41.288427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
1
250939 
0
 
9662
ValueCountFrequency (%) 
125093996.3%
 
096623.7%
 
2020-09-29T11:19:41.333112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.5 KiB
0
257924 
1
 
2677
ValueCountFrequency (%) 
025792499.0%
 
126771.0%
 
2020-09-29T11:19:41.378085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-09-29T11:19:06.653196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:06.835990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.018415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.217767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.413578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.619552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.805548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:07.989409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:08.167752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:08.351143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:08.547129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:08.733919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:08.922027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:09.122075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:09.315736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:09.494778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:09.679121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:09.857622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.035381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.222997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.409906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.604059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.787552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:10.975764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:11.153749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:11.337530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:11.516220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:11.691955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:11.870672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.050666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.233580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.407988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.596932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.772334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:12.958620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:13.135889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:13.315340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:13.511330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:13.709743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:13.931539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:14.163193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:14.355421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:14.541753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:14.734287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:14.922798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:15.104755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:15.277156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:15.454040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:15.645776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:15.829182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.022137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.205575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.390123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.576702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.760695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:16.942506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:17.132969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:17.340159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:17.522209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:17.712415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:18.463980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:18.647190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:18.824077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:19.013214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:19.206520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:19.396147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:19.600184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:19.818306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.032418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.215455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.409994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.594027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.769421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:20.939095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.110027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.291752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.460074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.639032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.807786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:21.976359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:22.141700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-29T11:19:41.629387image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-29T11:19:43.905378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-29T11:19:46.172274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-29T11:19:48.416501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-29T11:19:23.115271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-29T11:19:29.776734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentagehas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_othercount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_gradeland_surface_condition_nland_surface_condition_oland_surface_condition_tfoundation_type_hfoundation_type_ifoundation_type_rfoundation_type_ufoundation_type_wroof_type_nroof_type_qroof_type_xground_floor_type_fground_floor_type_mground_floor_type_vground_floor_type_xground_floor_type_zother_floor_type_jother_floor_type_qother_floor_type_sother_floor_type_xposition_jposition_oposition_sposition_tplan_configuration_aplan_configuration_cplan_configuration_dplan_configuration_fplan_configuration_mplan_configuration_nplan_configuration_oplan_configuration_qplan_configuration_splan_configuration_ulegal_ownership_status_alegal_ownership_status_rlegal_ownership_status_vlegal_ownership_status_w
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8441126075772192158601000010000100000000000200100100010100000100001000100000000010
9989500268869941013400000100000100000000000100101000100001001000001000100000000010

Last rows

building_idgeo_level_1_idgeo_level_2_idgeo_level_3_idcount_floors_pre_eqagearea_percentageheight_percentagehas_superstructure_adobe_mudhas_superstructure_mud_mortar_stonehas_superstructure_stone_flaghas_superstructure_cement_mortar_stonehas_superstructure_mud_mortar_brickhas_superstructure_cement_mortar_brickhas_superstructure_timberhas_superstructure_bamboohas_superstructure_rc_non_engineeredhas_superstructure_rc_engineeredhas_superstructure_othercount_familieshas_secondary_usehas_secondary_use_agriculturehas_secondary_use_hotelhas_secondary_use_rentalhas_secondary_use_institutionhas_secondary_use_schoolhas_secondary_use_industryhas_secondary_use_health_posthas_secondary_use_gov_officehas_secondary_use_use_policehas_secondary_use_otherdamage_gradeland_surface_condition_nland_surface_condition_oland_surface_condition_tfoundation_type_hfoundation_type_ifoundation_type_rfoundation_type_ufoundation_type_wroof_type_nroof_type_qroof_type_xground_floor_type_fground_floor_type_mground_floor_type_vground_floor_type_xground_floor_type_zother_floor_type_jother_floor_type_qother_floor_type_sother_floor_type_xposition_jposition_oposition_sposition_tplan_configuration_aplan_configuration_cplan_configuration_dplan_configuration_fplan_configuration_mplan_configuration_nplan_configuration_oplan_configuration_qplan_configuration_splan_configuration_ulegal_ownership_status_alegal_ownership_status_rlegal_ownership_status_vlegal_ownership_status_w
2605915608052036859801255301000000000111000000000310000100100100001000001000100000000010
26059220768310138219032255501000010000100000000000200100100100100000100001000100000000010
260593226421876786132513501000000000111000000000200100100100100000100001000100000000010
26059415955527181153760131200001000000100000000000200100100100100000001100000100000000010
260595827012826847182208501000000000100000000000300100100100100000100001000100000000010
26059668863625133516211556301000000000100000000000210000100100100001000001000000001000010
260597669485177152060206501000000000100000000000300100100100100000100001000100000000010
260598602512175181633556701000000000100000000000300100100010100000100001000100000000010
2605991514092639185121014600000100000100000000000200100100001001000010100000100000000010
26060074759421991013107601000000000300000000000310000100100100000100100000100000000010